structured_inhibition_spatial_network_model/scripts/competition_gain/run_fI_curve_neuron_model.py

from brian2 import *
import numpy as np

from scripts.models import hodgkin_huxley_eqs, hodgkin_huxley_params
from scripts.spatial_network.head_direction_index_over_noise_scale import calculate_rates

inputs = np.arange(0, 1, 0.025) * nA

eqs = hodgkin_huxley_eqs+'''
ih = 0*amp: amp'''
ng = NeuronGroup(len(inputs), model=eqs, method="exponential_euler", threshold="v>0*mV",
                 refractory="v>0*mV", namespace=hodgkin_huxley_params)
ng.I = inputs

spike_monitor = SpikeMonitor(ng)

net = Network(ng, spike_monitor)
net.run(1000 * ms)

rates = calculate_rates(spike_monitor.spike_trains().values())
plt.plot(inputs / nA, rates/hertz)
plt.ylim(0, np.max(rates/hertz)*1.05)
plt.xlim()
plt.grid()
plt.show()